/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    ReplaceMissingValues.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.WeightedAttributesHandler;
import weka.core.WeightedInstancesHandler;
import weka.filters.Sourcable;
import weka.filters.UnsupervisedFilter;

/**
 * <!-- globalinfo-start --> Replaces all missing values for nominal and numeric
 * attributes in a dataset with the modes and means from the training data. The
 * class attribute is skipped by default.
 * <p/>
 * <!-- globalinfo-end -->
 *
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -unset-class-temporarily
 *  Unsets the class index temporarily before the filter is
 *  applied to the data.
 *  (default: no)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class ReplaceMissingValues extends PotentialClassIgnorer implements UnsupervisedFilter, Sourcable, WeightedInstancesHandler, WeightedAttributesHandler {

    /** for serialization */
    static final long serialVersionUID = 8349568310991609867L;

    /** The modes and means */
    private double[] m_ModesAndMeans = null;

    /**
     * Returns a string describing this filter
     *
     * @return a description of the filter suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {

        return "Replaces all missing values for nominal and numeric attributes in a " + "dataset with the modes and means from the training data. The class attribute is skipped by default.";
    }

    /**
     * Returns the Capabilities of this filter.
     *
     * @return the capabilities of this object
     * @see Capabilities
     */
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        // attributes
        result.enableAllAttributes();
        result.enable(Capability.MISSING_VALUES);

        // class
        result.enableAllClasses();
        result.enable(Capability.MISSING_CLASS_VALUES);
        result.enable(Capability.NO_CLASS);

        return result;
    }

    /**
     * Sets the format of the input instances.
     *
     * @param instanceInfo an Instances object containing the input instance
     *                     structure (any instances contained in the object are
     *                     ignored - only the structure is required).
     * @return true if the outputFormat may be collected immediately
     * @throws Exception if the input format can't be set successfully
     */
    public boolean setInputFormat(Instances instanceInfo) throws Exception {

        super.setInputFormat(instanceInfo);
        setOutputFormat(instanceInfo);
        m_ModesAndMeans = null;
        return true;
    }

    /**
     * Input an instance for filtering. Filter requires all training instances be
     * read before producing output.
     *
     * @param instance the input instance
     * @return true if the filtered instance may now be collected with output().
     * @throws IllegalStateException if no input format has been set.
     */
    public boolean input(Instance instance) {

        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }
        if (m_NewBatch) {
            resetQueue();
            m_NewBatch = false;
        }
        if (m_ModesAndMeans == null) {
            bufferInput(instance);
            return false;
        } else {
            convertInstance(instance);
            return true;
        }
    }

    /**
     * Signify that this batch of input to the filter is finished. If the filter
     * requires all instances prior to filtering, output() may now be called to
     * retrieve the filtered instances.
     *
     * @return true if there are instances pending output
     * @throws IllegalStateException if no input structure has been defined
     */
    public boolean batchFinished() {

        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }

        if (m_ModesAndMeans == null) {
            // Compute modes and means
            double sumOfWeights = getInputFormat().sumOfWeights();
            double[][] counts = new double[getInputFormat().numAttributes()][];
            for (int i = 0; i < getInputFormat().numAttributes(); i++) {
                if (getInputFormat().attribute(i).isNominal()) {
                    counts[i] = new double[getInputFormat().attribute(i).numValues()];
                    if (counts[i].length > 0)
                        counts[i][0] = sumOfWeights;
                }
            }
            double[] sums = new double[getInputFormat().numAttributes()];
            for (int i = 0; i < sums.length; i++) {
                sums[i] = sumOfWeights;
            }
            double[] results = new double[getInputFormat().numAttributes()];
            for (int j = 0; j < getInputFormat().numInstances(); j++) {
                Instance inst = getInputFormat().instance(j);
                for (int i = 0; i < inst.numValues(); i++) {
                    if (!inst.isMissingSparse(i)) {
                        double value = inst.valueSparse(i);
                        if (inst.attributeSparse(i).isNominal()) {
                            if (counts[inst.index(i)].length > 0) {
                                counts[inst.index(i)][(int) value] += inst.weight();
                                counts[inst.index(i)][0] -= inst.weight();
                            }
                        } else if (inst.attributeSparse(i).isNumeric()) {
                            results[inst.index(i)] += inst.weight() * inst.valueSparse(i);
                        }
                    } else {
                        if (inst.attributeSparse(i).isNominal()) {
                            if (counts[inst.index(i)].length > 0) {
                                counts[inst.index(i)][0] -= inst.weight();
                            }
                        } else if (inst.attributeSparse(i).isNumeric()) {
                            sums[inst.index(i)] -= inst.weight();
                        }
                    }
                }
            }
            m_ModesAndMeans = new double[getInputFormat().numAttributes()];
            for (int i = 0; i < getInputFormat().numAttributes(); i++) {
                if (getInputFormat().attribute(i).isNominal()) {
                    if (counts[i].length == 0)
                        m_ModesAndMeans[i] = Utils.missingValue();
                    else
                        m_ModesAndMeans[i] = (double) Utils.maxIndex(counts[i]);
                } else if (getInputFormat().attribute(i).isNumeric()) {
                    if (Utils.gr(sums[i], 0)) {
                        m_ModesAndMeans[i] = results[i] / sums[i];
                    }
                }
            }

            // Convert pending input instances
            for (int i = 0; i < getInputFormat().numInstances(); i++) {
                convertInstance(getInputFormat().instance(i));
            }
        }
        // Free memory
        flushInput();

        m_NewBatch = true;
        return (numPendingOutput() != 0);
    }

    /**
     * Convert a single instance over. The converted instance is added to the end of
     * the output queue.
     *
     * @param instance the instance to convert
     */
    private void convertInstance(Instance instance) {

        Instance inst = instance;
        boolean hasMissing = instance.hasMissingValue();
        if (hasMissing) {
            if (instance instanceof SparseInstance) {
                double[] vals = new double[instance.numValues()];
                int[] indices = new int[instance.numValues()];
                int num = 0;
                for (int j = 0; j < instance.numValues(); j++) {
                    if (instance.isMissingSparse(j) && (getInputFormat().classIndex() != instance.index(j)) && (instance.attributeSparse(j).isNominal() || instance.attributeSparse(j).isNumeric())) {
                        if (m_ModesAndMeans[instance.index(j)] != 0.0) {
                            vals[num] = m_ModesAndMeans[instance.index(j)];
                            indices[num] = instance.index(j);
                            num++;
                        }
                    } else {
                        vals[num] = instance.valueSparse(j);
                        indices[num] = instance.index(j);
                        num++;
                    }
                }
                if (num == instance.numValues()) {
                    inst = new SparseInstance(instance.weight(), vals, indices, instance.numAttributes());
                } else {
                    double[] tempVals = new double[num];
                    int[] tempInd = new int[num];
                    System.arraycopy(vals, 0, tempVals, 0, num);
                    System.arraycopy(indices, 0, tempInd, 0, num);
                    inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
                }
            } else {
                double[] vals = new double[getInputFormat().numAttributes()];
                for (int j = 0; j < instance.numAttributes(); j++) {
                    if (instance.isMissing(j) && (getInputFormat().classIndex() != j) && (getInputFormat().attribute(j).isNominal() || getInputFormat().attribute(j).isNumeric())) {
                        vals[j] = m_ModesAndMeans[j];
                    } else {
                        vals[j] = instance.value(j);
                    }
                }
                inst = new DenseInstance(instance.weight(), vals);
            }
        }
        inst.setDataset(instance.dataset());
        push(inst, !hasMissing); // No need to shallow copy if we've deep copied already
    }

    /**
     * Returns a string that describes the filter as source. The filter will be
     * contained in a class with the given name (there may be auxiliary classes),
     * and will contain two methods with these signatures:
     * 
     * <pre>
     * <code>
     * // converts one row
     * public static Object[] filter(Object[] i);
     * // converts a full dataset (first dimension is row index)
     * public static Object[][] filter(Object[][] i);
     * </code>
     * </pre>
     * 
     * where the array <code>i</code> contains elements that are either Double,
     * String, with missing values represented as null. The generated code is public
     * domain and comes with no warranty.
     *
     * @param className the name that should be given to the source class.
     * @param data      the dataset used for initializing the filter
     * @return the object source described by a string
     * @throws Exception if the source can't be computed
     */
    public String toSource(String className, Instances data) throws Exception {
        StringBuffer result;
        boolean[] numeric;
        boolean[] nominal;
        String[] modes;
        double[] means;
        int i;

        result = new StringBuffer();

        // determine what attributes were processed
        numeric = new boolean[data.numAttributes()];
        nominal = new boolean[data.numAttributes()];
        modes = new String[data.numAttributes()];
        means = new double[data.numAttributes()];
        for (i = 0; i < data.numAttributes(); i++) {
            numeric[i] = (data.attribute(i).isNumeric() && (i != data.classIndex()));
            nominal[i] = (data.attribute(i).isNominal() && (i != data.classIndex()));

            if (numeric[i])
                means[i] = m_ModesAndMeans[i];
            else
                means[i] = Double.NaN;

            if (nominal[i])
                modes[i] = data.attribute(i).value((int) m_ModesAndMeans[i]);
            else
                modes[i] = null;
        }

        result.append("class " + className + " {\n");
        result.append("\n");
        result.append("  /** lists which numeric attributes will be processed */\n");
        result.append("  protected final static boolean[] NUMERIC = new boolean[]{" + Utils.arrayToString(numeric) + "};\n");
        result.append("\n");
        result.append("  /** lists which nominal attributes will be processed */\n");
        result.append("  protected final static boolean[] NOMINAL = new boolean[]{" + Utils.arrayToString(nominal) + "};\n");
        result.append("\n");
        result.append("  /** the means */\n");
        result.append("  protected final static double[] MEANS = new double[]{" + Utils.arrayToString(means).replaceAll("NaN", "Double.NaN") + "};\n");
        result.append("\n");
        result.append("  /** the modes */\n");
        result.append("  protected final static String[] MODES = new String[]{");
        for (i = 0; i < modes.length; i++) {
            if (i > 0)
                result.append(",");
            if (nominal[i])
                result.append("\"" + Utils.quote(modes[i]) + "\"");
            else
                result.append(modes[i]);
        }
        result.append("};\n");
        result.append("\n");
        result.append("  /**\n");
        result.append("   * filters a single row\n");
        result.append("   * \n");
        result.append("   * @param i the row to process\n");
        result.append("   * @return the processed row\n");
        result.append("   */\n");
        result.append("  public static Object[] filter(Object[] i) {\n");
        result.append("    Object[] result;\n");
        result.append("\n");
        result.append("    result = new Object[i.length];\n");
        result.append("    for (int n = 0; n < i.length; n++) {\n");
        result.append("      if (i[n] == null) {\n");
        result.append("        if (NUMERIC[n])\n");
        result.append("          result[n] = MEANS[n];\n");
        result.append("        else if (NOMINAL[n])\n");
        result.append("          result[n] = MODES[n];\n");
        result.append("        else\n");
        result.append("          result[n] = i[n];\n");
        result.append("      }\n");
        result.append("      else {\n");
        result.append("        result[n] = i[n];\n");
        result.append("      }\n");
        result.append("    }\n");
        result.append("\n");
        result.append("    return result;\n");
        result.append("  }\n");
        result.append("\n");
        result.append("  /**\n");
        result.append("   * filters multiple rows\n");
        result.append("   * \n");
        result.append("   * @param i the rows to process\n");
        result.append("   * @return the processed rows\n");
        result.append("   */\n");
        result.append("  public static Object[][] filter(Object[][] i) {\n");
        result.append("    Object[][] result;\n");
        result.append("\n");
        result.append("    result = new Object[i.length][];\n");
        result.append("    for (int n = 0; n < i.length; n++) {\n");
        result.append("      result[n] = filter(i[n]);\n");
        result.append("    }\n");
        result.append("\n");
        result.append("    return result;\n");
        result.append("  }\n");
        result.append("}\n");

        return result.toString();
    }

    /**
     * Main method for testing this class.
     *
     * @param argv should contain arguments to the filter: use -h for help
     */
    public static void main(String[] argv) {
        runFilter(new ReplaceMissingValues(), argv);
    }
}
